The following instructions are aimed at explaining how to reproduce the results presented in the paper "NEVER FORGET THE FIRST TIME: THE PERSISTENT EFFECT OF CORRUPTION AND THE RISE OF POPULISM IN ITALY".

The analysis is run on MacOS Big Sur by using STATA 17.

The replication files include:

- A folder named "data" containing all the datasets necessary to replicate the analysis
- A folder named "do_files" containing the Stata code "replication" to replicate the entire analysis
- An empty folder named "results" for collecting the graphical outputs of the analysis once they are generated using the replication code


Instruction for replication:

1 - Unzip the file "Never_Forget_The_First_Time"

2 - To reproduce the main analysis of the paper (i.e Figures 1-3 and Tables 1-3), as well as the analysis presented in the online appendix (i.e, Figures A.1-A.9 and Tables A.1-A.20), please open and run the do file "replication" in the folder "do_files", after changing the working directories at the beginning of the do file. The stata packages that need to be installed before running the code are listed at the beginning of the do file.


The following lists include a brief description of all variables employed in the analysis, further disentangled by dataset in which they appear:


Dataset "trustlab.dta":

	trust_gov: Trust in government
	trust_parl: Trust in Parliament
	trust_police: Trust in Police
	trust_media: Trust in Media
	trust_fininst: Trust in Financial Institutions
	trust_civser: Trust in Civil Servants
	inst_lt_obj: Institutions are forward-looking
	inst_integr: Institutions are interger
	inst_transp: Institution are transparent
	o_mean: respondent personality trait: openness 
	c_mean: respondent personality trait: conscientiousness 
	e_mean: respondent personality trait: extraversion
	a_mean: respondent personality trait: agreeableness
	n_mean: respondent personality trait: neuroticism
	info_tv: How much information about current events the respondent gets from tv?
	info_internet: How much information about current events the respondent gets from internet?
	female: Respondent's gender
	code_region_resid_istat: Respondent's region of current residence
	code_prov_child_istat: Respondent's province of current residence
	code_region_child_istat: Respondent's region of childhood
	code_macroreg_child_istat: Respondent's macro-region of childhood
	unemployed: Is the respondent unemployed?
	boost: Is the respondent belonging to the boosting sample?
	education: Respondent's highest level of education achieved
	public_sec: Does the respondent work in the public sector?
	vote2018: Did the respondent vote at the 2018 national elections?
	country: respondent's country of residence
	italy: Does the respondent live in Italy?
	cou_1: Does the respondent live in Germany?
	cou_2: Does the respondent live in US?
	cou_3: Does the respondent live in Korea?
	cou_4: Does the respondent live in France?
	cou_5: Does the respondent live in Slovenia?
	cou_7: Does the respondent live in UK?
	d_gdp: Change in GDP between 2006-2013 within respondent's region of residence
	age_int: Respondent's age
	birth_year: Respondent's year of birth
	d_unemp: Change in unemployment between 1993-1995 within respondent's region of childhood
	party_1: Did the respondent vote for LEU at the 2018 national elections?
	party_2: Did the respondent vote for PD at the 2018 national elections?
	party_3: Did the respondent vote for M5S at the 2018 national elections?
	party_4: Did the respondent vote for LEGA at the 2018 national elections?
	party_5: Did the respondent vote for FI at the 2018 national elections?
	party_6: Did the respondent vote for FDI at the 2018 national elections?
	party_7: Did the respondent vote for +EUROPA at the 2018 national elections?
	populists: Did the respondent vote for a populist party at the 2018 national elections?
	first: Respondent's cohort of first time voters
	birth_dec: Respondent's decade of birth
	T: Does the respondent's belong to the the 1975/1976 cohort?
	catho: Is the respondent catholic?
	south: Does the respondent live in the South?
	stragi: There was a mafia terrorist attack in the respondent's municipality of childhood
	edufirst: Respondent completed secondary education
	edufirst_2: Respondent completed secondary education
	firms1991: Number of firms per capita in 1991 within the respondent's region of childhood
	edu1991: Share of graduates in 1991 within the respondent's region of childhood
	lab1991: Share of active populationin 1991 within the respondent's region of childhood
	old1991: Share of people aged 65+ in 1991 within the respondent's region of childhood
	d_pil_xc: Change in GDP per capita between 1993-1995 within the respondent's region of childhood
	t_rev: Reverse treatment
	q_corr_2: Respondent's region of childhood above the median of the distribution of MPs charged for corruption during Clean Hands
	nofollow: No answer to the follow-up survey
	party: Party voted at the 2018 national elections
	inter_south: Interaction between the variable T and south
	inter_mafia: Interaction between the variable T and stragi
	inter_unemp: Interaction between the variable T and d_unemp
	inter_gdp: Interaction between the variable T and d_pil_xc
	income_ln: Respondent's personal income (Ln)


Dataset "itanes1994.dta":

	religious_group: Respondent's religious group
	education: Respondent's highest level of education achieved
	birth: Respondent's year of birth
	birth_dec: Respondent's decade of birth
	T: Does the respondent's belong to the the 1975/1976 cohort?
	comizzi: Did the respondent partecipate to political rallies?
	comizzi_tv: Did the respondent watch to political rallies on TV?
	convinto: Did the respondent convinced someone to vote for her preferred party?
	cercato: Did the respondents search for political information?
	poster: Did the respondents stick political posters?
	gender: Respondent's gender
	region: Respondent's region of residence
	t_rev: Reverse treatment


Dataset "itanes1996.dta":

	religious_group: Respondent's religious group
	education: Respondent's highest level of education achieved
	birth: Respondent's year of birth
	birth_dec: Respondent's decade of birth 
	ditta: Distrust in democracy
	T: Does the respondent's belong to the the 1975/1976 cohort?
	pol_int2: Interest in politics
	pol_int: Read news on politics
	lega_1: LEGA is the closest party
	an_1: AN/MSI is the closest party
	dc_2: Respondent voted for DC at the 1994 national elections
	psi_2: Respondent voted for PSI at the 1994 national elections 
	lega_2: Respondent voted for LEGA at the 1994 national elections
	an_2: Respondent voted for AN/MSI at the 1994 national elections
	gender: Respondent's gender
	region: Respondent's region of residence
	t_rev: Reverse treatment 
	abstant: Respondent abstained from voting at the 1994 elections


Dataset "itanes2001.dta":

	regione: Respondent's region of residence
	age: Respondent's age
	birth: Respondent's year of birth
	birth_dec: Respondent's decade of birth  	
	T: Does the respondent's belong to the the 1975/1976 cohort?
 	trust_parl: Trust in Parliament
	female: Respondent's gender
 	catho: Is the respondent catholic?
	year: Year of the survey 		
	t_rev: Reverse treatment 
 	educ2: Respondent completed secondary education


Dataset "itanes2018.dta":

	regione: Respondent's region of residence
	age: Respondent's age
	birth: Respondent's year of birth
	birth_dec: Respondent's decade of birth  	
	T: Does the respondent's belong to the the 1975/1976 cohort?
 	trust_parl: Trust in Parliament
	trust_part: Trust in Political Parties 
	female: Respondent's gender
 	catho: Is the respondent catholic?
	year: Year of the survey 		
	t_rev: Reverse treatment 
 	educ2: Respondent completed secondary education  	
	populists: Did the respondent vote for a populist party at the 2018 national elections?


Dataset "itanes2001_2018.dta":

	regione: Respondent's region of residence
	age: Respondent's age
	birth: Respondent's year of birth
	birth_dec: Respondent's decade of birth  	
	T: Does the respondent's belong to the the 1975/1976 cohort?
 	trust_parl: Trust in Parliament
	female: Respondent's gender
 	catho: Is the respondent catholic?
	year: Year of the survey 		
	t_rev: Reverse treatment 
 	educ2: Respondent completed secondary education 


Dataset "fig1panelA.dta":

	leg: Number of legislature
	core: Number of RAP

Dataset "fig1panelB.dta":

	year: Year
	corruzione: Number of article about corruption

Dataset "fig1panelC.dta":

	year: Year
	corruzione: Number of appearance of corruption in Italian books
	corruzione: Number of appearance of soccer in Italian books

Dataset "fig1panelD.dta":

	date: Date of transmission
	duration: Duration of transmission

Dataset "figA1.dta":

	year: Year
	hardly: Politicians are hardly corrupt
	index: Trust in Federal Government

Dataset "figA2.dta":

	year: Year
	party: Political party 
	charged: Number of charges for corruption

Dataset "figA5panelA.dta":

	date: Date of transmission
	duration: Duration of transmission

Dataset "figA5panelB.dta":

	date: Date of transmission
	duration: Duration of transmission

Dataset "figA6.dta":

	num_uscite_corriere: Number of issues per month
	Corruzione_Corrieredellasera: Number of articles about corruption appearing in the first page
	month: Month
	year: Year
	Crisi_Corrieredellasera: Number of articles about economic crisis appearing in the first page
	Strage_Corrieredellasera: Number of articles about mafia terrorist attacks appearing in the first page

Dataset "figA7.dta":

	country: Country
	year: Year
	voteshare: Vote share received at the last elections 
	flag: Right-wing parties vs Left-wing parties

Dataset "figA8.dta":

	election_year: Year of elections
	flag: Group of cohorts
	populist: Vote share for populist parties
